Streaming-Based Artist Resource Allocation With Predictive Engagement
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Solution Overview
Problem
Conventional media content service provider systems face inefficiencies in crediting artists for media content engagement due to delays in resource allocation, leading to increased computational and network congestion, as well as the need for multiple intermediaries that result in delayed payments and inefficient use of resources.
Innovation Solution
Implementing a data-driven approach with machine learning techniques to predict streaming counts and provide real-time resource allocations directly to artists, reducing the need for intermediaries and minimizing delays by using a single platform that integrates media content and payment services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional media content service provider systems use multiple intermediaries for resource allocation, then resource distribution can be managed through established channels, but payment delays and computational congestion increase
Solution Approach 1:
The patent extracts and eliminates the intermediary entities from the resource allocation chain. By implementing a direct resource allocation system where the media content service provider system communicates directly with artist accounts, it removes the time-consuming handoffs between multiple intermediaries while maintaining reliable resource distribution through established platform protocols
Solution Approach 2:
The system performs preliminary resource allocation by predicting future streaming counts using machine learning models. Resources are allocated in advance based on predicted engagement metrics before the actual streaming occurs, eliminating the need for delayed post-event processing and enabling real-time or near-real-time payment delivery
2Use of energy by moving object
If conventional systems process resource allocations periodically, then computational resources can be managed efficiently, but network congestion and processing delays increase
Solution Approach 1:
The patent transforms the static periodic resource allocation into a dynamic system that adapts to real-time streaming engagement. Machine learning models continuously predict streaming counts and trigger resource allocations dynamically based on actual engagement patterns, enabling the system to process allocations at optimal intervals rather than fixed schedules, thus improving both speed and computational efficiency
Solution Approach 2:
The system changes the parameter of resource allocation timing from fixed periodic intervals to variable intervals based on predicted streaming engagement metrics. By using machine learning models to forecast streaming counts, the system adjusts allocation timing dynamically, processing resources faster during high-engagement periods while maintaining computational efficiency during lower-activity periods
3Ease of manufacture
If multiple intermediaries are involved in resource allocation, then resource distribution can be managed through established channels, but device complexity and processing overhead increase
Solution Approach 1:
The patent merges the resource allocation function directly into the media content service provider system, eliminating separate intermediary processing systems. By combining content delivery and resource allocation functions into a single integrated platform, the system reduces architectural complexity while maintaining ease of implementation through unified processing logic and shared infrastructure
Data Source
AI summary
A technique for resource allocation estimation for media content items is described. In accordance with the described techniques engagement by a set of user accounts with respective media content items of at least one media content service provider system is obtained. The media content service provider system and/or a payment service system generates historical streaming data for the respective media content items based on the engagement of the set of user accounts. An estimated streaming count of a media content item over a time period based on the historical streaming data for the respective media content items is determined. An estimated resource allocation for the artist is determined based on the estimated streaming count and an advance of funds is facilitated based on the estimated resource allocation to an account of the artist during the time period.